Practical aspects of the DesignDEVS simulation environment
Bibliographic record
Abstract
DesignDEVS is a simulation development environment based on the Discrete Event System Specification (DEVS) formalism. This paper provides an in-depth overview of the software while focusing on the practical considerations influencing its design. Practitioners who stand to benefit from systems engineering will approach formalism-based simulation tools with little knowledge of the underlying theory. It is therefore important that theoretical principles, such as the separation of model and simulator, be emphasized by the user interface. Other practical aspects of DesignDEVS include the simplicity of atomic model code, a focus on coupling for collaboration purposes, the enforcement of essential modeling constraints, and a reliance on best practices in cases where strict enforcement might inconvenience users. In DesignDEVS, an issue we refer to as the Insidious Pointer Problem is aggressively tackled through run-time error handling. By contrast, the separation of output values from state transitions is left as a best practice for the sake of user convenience. The design decisions explained in this paper are relevant to developers of other formalism-based tools seeking widespread adoption of scalable modeling and simulation practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".